1. Field
The present disclosure relates generally to search engine content management systems, and, more particularly, to a Jabba-type contextual tagger for use in or with search engine content management systems.
2. Information
The Internet is widespread. The World Wide Web or simply the Web, provided by the Internet, is growing rapidly, at least in part, from the large amount of content being added seemingly on a daily basis. A wide variety of content, such as one or more electronic documents, for example, is continually being identified, located, retrieved, accumulated, stored, or communicated. Various information databases including, for example, knowledge bases or other collections of content, Web-based or otherwise, have become commonplace, as have related communication networks or computing resources that help users to access relevant information. Effectively or efficiently identifying or locating content of interest may facilitate or support information-seeking behavior of users and may lead to an increased usability of a search engine.
With a large amount of content being available, a number of tools may often be provided to allow for copious amounts of information to be searched through in an efficient or effective manner. For example, service providers may allow users to search the Web or other networks, databases or other data repositories, etc. using search engines. In some instances, a search engine may, for example, utilize one or more sequence or like taggers, such as a Hidden Markov Model (HMM), Conditional Random Fields (CRF), or the like to facilitate or support locating relevant content. At times, however, sequence or like taggers may require machine learning from a statistically sufficient amount of training data, for example, which may be labor-intensive. In addition, an output of a sequence or like tagger may be the result of a statistical process employing certain approximations or assumptions, such as with respect to sequence distributions, for example, which may be computationally-expensive or otherwise error-prone. A dictionary-type tagging may be less ambiguous, but may label words or phrases corresponding to a search query regardless of its context, however.